Open-source text embedding model that outperforms OpenAI models on key benchmarks
A large context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks. Specialized for generating text embeddings, semantic search, and RAG applications.
137 million
8192 tokens
Ideal for retrieval-augmented generation (RAG), semantic search, clustering, and document similarity tasks.
Multilingual
Installation:
pip install tinfoil
Inference:
from tinfoil import TinfoilAI
client = TinfoilAI(
enclave="nomic-embed-text.model.tinfoil.sh",
repo="tinfoilsh/confidential-nomic-embed-text",
api_key="YOUR_API_KEY",
)
embedding = client.embeddings.create(
model="nomic-embed-text",
input="The quick brown fox jumps over the lazy dog.",
)
print(embedding.data[0].embedding)